2026· Computers, Materials & Continua· 0 citations· 23 references
TL;DR
This paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions, and explicitly targets adversarial robustness in financial fraud detection.
Abstract
: As artificial intelligence becomes increasingly embedded in financial systems, ensuring the security and robustness of these models is critical, particularly in sensitive tasks like credit card fraud detection. Despite their predictive success, deep learning models remain vulnerable to adversarial examples: subtly manipulated inputs that can mislead classification outcomes. Unlike existing approaches that typically rely on either adversarial training or standalone input filtering, this paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions. Using a real-world, imbalanced credit card transaction dataset of 284,807 transactions, the proposed method achieves superior performance on clean data with an accuracy of 0.991, F1-score of 0.872, and Area Under the Precision–Recall Curve (AUC-PR) of 0.952. Under adversarial conditions, the framework maintains robustness, achieving an F1-score of 0.648 against Fast Gradient Sign Method (FGSM) and 0.610 against Projected Gradient Descent (PGD) attacks, outperforming baseline models by margins of > 0.10 in F1. In contrast to prior work that primarily focuses on predictive performance or single-defense strategies, the proposed approach explicitly targets adversarial robustness in financial fraud detection through a complementary integration of defense mechanisms. Ablation studies confirm the complementary effect of adversarial training and DAE-based filtering, while detection analysis shows an adversarial detection accuracy of 87.8%. These findings highlight the practicality of hybrid defense strategies for improving the trustworthiness of AI systems in finance.
This study evaluated the adversarial robustness of machine learning-based fraud detection systems by comparing classifier vulnerability profiles and assessing adversarial training as a mitigation strategy. Using the IEEE-CIS Fraud Detection dataset, comprising 590,540 transactions with a fraud incidence of 3.5%, four classifiers—logistic regression, random forest, gradient boosting, and a feed-forward neural network—were trained under identical preprocessing and class-weighting conditions and then subjected to Fast Gradient Sign Method and Projected Gradient Descent attacks at a perturbation budget of 0.02. Adversarial examples were constructed directly using closed-form and backpropagated gradients for the differentiable classifiers and using a logistic regression surrogate for the non-differentiable ensembles, before adversarial training was applied as a post-attack mitigation stage. Logistic regression proved the most adversarially vulnerable architecture, sustaining a 31.12-percentage-point recall loss under Projected Gradient Descent, while adversarial training subsequently restored its recall from 0.39 to 0.999 at an accuracy cost of 0.10 percentage points. Random forest and gradient boosting were not degraded by the surrogate-based attack, indicating that comparative robustness claims for tree-based ensembles require attack methods suited to their non-differentiable structure rather than transfer-based evaluation alone. Within the scope of this single-dataset evaluation, the findings support the adoption of adversarial training for gradient-based fraud detection models and suggest that robustness claims should be accompanied by disclosure of the attack methodology used to establish them.
Ololade Zainab Adesokan, Abiola Omolola Bamsa, O. Obioha-Val et al.· Journal of Engineering Resea...· 0 citations
This paper investigates the adversarial robustness of various machine learning models applied to synthetic identity fraud detection in credit risk settings, and proposes and assess defense mechanisms, including adversarial training and robust feature engineering, to enhance model resilience.
Arvind Kumar Singh, Lakshmi Narayanan· International Journal of Com...· 0 citations
Financial fraud in digital payment systems is a major cybersecurity issue. Global losses exceed $32 billion each year, and fraud-detection methods are constantly improving to keep pace with increasingly complex attack patterns. One significant challenge in fraud analytics is the severe class imbalance. Fraudulent transactions make up a very small fraction of total transaction volume. This study introduces a new detection framework that combines Generative Adversarial Networks (GANs) for synthesising minority classes with Random Forest (RF) ensemble learning for strong classification. The GAN part is based on adversarial training methods introduced earlier, with improved stabilisation techniques from recent studies and tabular data modelling strategies from previous research. The Random Forest classifier uses the ensemble approach first defined in earlier work. In this paper, a synthetic dataset featuring 20,000 transactions and 20 engineered features is presented. This set includes transactional, behavioural, device-based, and contextual information. The GAN uses a 100-dimensional latent-space generator and a binary discriminator, trained for 1,000 epochs with the Adam optimiser. We tuned the hyperparameters of the RF classifier using GridSearchCV with 5-fold cross-validation, resulting in the best parameters: n_estimators=100, max_depth=20, and min_samples_split=5. These experiments show an overall accuracy of 97.09%, with balanced precision and recall metrics at 0.97. This outperforms the baseline RF (96.75%), SMOTE-RF (96.82%), and XGBoost (96.91%). Adding 5,000 GAN-synthesised minority samples, generated using adversarial oversampling techniques, increased validation accuracy to 97.12% (p < 0.05, McNemar’s test). Analysis of feature importance showed that geo-location anomaly flags (24.95%) and previous fraudulent behaviour indicators (20.41%) were the most distinguishing attributes. The proposed hybrid GAN-RF framework effectively addresses class imbalance while maintaining computational efficiency and model interpretability. It shows strong promise for use in real-time fraud detection in Unified Payments Interface (UPI) environments.
Hiteshkumar M. Nimbark, Hansiniba P. Jadeja, Evan H. Habibani· DMPedia Lecture Notes in Com...· 0 citations
Machine learning models are widely used in financial fraud and credit-risk detection, yet their adversarial robustness remains difficult to evaluate because financial tabular data involve domain-specific constraints, severe class imbalance, and asymmetric attacker capability. We argue that, in this setting, robustness is not only an attribute of the model, but also an attribute of the evaluation protocol. Different ways of enforcing constraints and capability can lead to substantially different robustness conclusions. This paper presents FraudBench, a protocol-sensitive benchmark for adversarial robustness evaluation in financial fraud and credit-risk detection. Rather than treating domain constraints as post-hoc validity checks, FraudBench evaluates the same dataset--model--attack--defence setting under three matched protocols: unconstrained attacks, post-hoc feasibility filtering, and deployment-aware constraint-integrated attacks. FraudBench covers four public financial datasets, and evaluates neural, tree-based, and ensemble models using three attack settings. Our results show that robustness conclusions are highly protocol-sensitive. On Lending Club Loan Data under the white-box setting, post-hoc filtering leaves only 3.7 feasible-flipped examples on average, whereas in-attack projection with attacker mutability masking produces 2,832.3 feasible-flipped examples under the same perturbation budget. The results on IEEE-CIS further show that feasibility and attacker capability are separate axes, while black-box evaluation shows that protocol choice can alter model-family rankings. These findings suggest that fraud robustness evaluation should report predictive degradation and attack feasibility jointly, and should incorporate domain constraints into attack generation rather than treating them as post-processing checks.
Xitong Zeng, Zhaoge Bi, Yi-Tian Yang et al.· 0 citations
A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system.
Harsh Verma· International Journal of Sci...· 0 citations
This paper introduces DefendMal, a novel framework that synergistically combines Denoise Autoencoder with Sequence Squeezing, a Context-aware Adversarial Generator (CAG-AdvGAN), Projected Gradient Descent (PGD) adversarial training, and a Positive–Negative Detector with Variational Autoencoder (PNDetector-VAE) to enhance robustness against evolving adversarial threats.
Dennis Benedict Crasta, Vikash Kumar· Journal of Computer Virology...· 0 citations
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